5 citations · 7 across the 2 of their papers we have counts for
5 papers
Meta-Solver for Neural Ordinary Differential Equations
Julia Gusak, Alexandr Katrutsa, Talgat Daulbaev +2
A conventional approach to train neural ordinary differential equations (ODEs) is to fix an ODE solver and then learn the neural network's weights to optimize a target loss functio…
Towards Understanding Normalization in Neural ODEs
Julia Gusak, Larisa Markeeva, Talgat Daulbaev +3
Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly…
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva +3
We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with the reverse dynamic method (known in the litera…
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Chunfeng Cui, Kaiqi Zhang, Talgat Daulbaev +3
Active subspace is a model reduction method widely used in the uncertainty quantification community. In this paper, we propose analyzing the internal structure and vulnerability an…
Reduced-Order Modeling of Deep Neural Networks
Julia Gusak, Talgat Daulbaev, Evgeny Ponomarev +2
We introduce a new method for speeding up the inference of deep neural networks. It is somewhat inspired by the reduced-order modeling techniques for dynamical systems.The cornerst…